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swin-base-food101-int8-structured44.5-20eph

Image Classification
Transformers
PyTorch
OpenVINO
swin
vision
generated_from_trainer
Eval Results
Inference Endpoints
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swin-base-food101-int8-structured44.5-20eph / README.md
Vui Seng Chua
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metadata
license: apache-2.0
tags:
  - image-classification
  - vision
  - generated_from_trainer
datasets:
  - food101
metrics:
  - accuracy
model-index:
  - name: jpqd-swin-b-20eph-r1.00-s2e5-mock-main-merge-pr2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: food101
          type: food101
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9179009900990099

jpqd-swin-b-20eph-r1.00-s2e5-mock-main-merge-pr2

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the food101 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2715
  • Accuracy: 0.9179

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 128
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
5.1452 0.42 500 5.4928 0.6440
0.9839 0.84 1000 0.7956 0.8580
0.8533 1.27 1500 0.4392 0.8911
0.6123 1.69 2000 0.3768 0.8983
12.3076 2.11 2500 12.0798 0.8953
49.301 2.54 3000 48.6292 0.8343
75.6345 2.96 3500 75.7027 0.6777
94.2556 3.38 4000 93.5852 0.5604
103.3226 3.8 4500 103.1255 0.5702
107.3423 4.23 5000 107.9250 0.5359
108.9013 4.65 5500 108.5225 0.5882
2.045 5.07 6000 1.1149 0.8154
1.3377 5.49 6500 0.6747 0.8665
0.7565 5.92 7000 0.5814 0.8765
0.7493 6.34 7500 0.5460 0.8840
0.7693 6.76 8000 0.5109 0.8851
0.6082 7.19 8500 0.4893 0.8895
0.7575 7.61 9000 0.4521 0.8943
0.7943 8.03 9500 0.4465 0.8941
0.5521 8.45 10000 0.4119 0.8967
0.6536 8.88 10500 0.4071 0.9010
0.5164 9.3 11000 0.3945 0.9010
0.6687 9.72 11500 0.3884 0.9030
0.4374 10.14 12000 0.3764 0.9040
0.7326 10.57 12500 0.3678 0.9060
0.6148 10.99 13000 0.3602 0.9057
0.6068 11.41 13500 0.3566 0.9075
0.6105 11.83 14000 0.3456 0.9074
0.5277 12.26 14500 0.3383 0.9107
0.5255 12.68 15000 0.3328 0.9097
0.4536 13.1 15500 0.3268 0.9108
0.5337 13.52 16000 0.3256 0.9107
0.5299 13.95 16500 0.3161 0.9124
0.3037 14.37 17000 0.3162 0.9123
0.4171 14.79 17500 0.3078 0.9124
0.5375 15.22 18000 0.3002 0.9116
0.2722 15.64 18500 0.2953 0.9134
0.3684 16.06 19000 0.2960 0.9137
0.4369 16.48 19500 0.2918 0.9150
0.3346 16.91 20000 0.2856 0.9171
0.3645 17.33 20500 0.2856 0.9162
0.4475 17.75 21000 0.2833 0.9157
0.2553 18.17 21500 0.2788 0.9167
0.5098 18.6 22000 0.2766 0.9164
0.4149 19.02 22500 0.2732 0.9177
0.3737 19.44 23000 0.2734 0.9181
0.325 19.86 23500 0.2715 0.9176

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.10.1
  • Tokenizers 0.13.2